{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proj5\n",
    "本项目通过程序仿真的方式实现了光束在自由空间中衍射的过程，衍射过程可以通过衍射光学元件 (Diffractive Optical Elements, DOE)人为调控，DOE通常采用微纳刻蚀工艺构成二维分布的衍射单元，每个衍射单元可以有特定的形貌、折射率等，对激光波前位相分布进行精细调控。激光经过每个衍射单元后发生衍射，并在一定距离（通常为无穷远或透镜焦平面）处产生干涉，形成特定的光强分布。\n",
    "\n",
    "本项目基于光学神经网络实现了在手写数字识别数据集(MNIST)上的图像分类功能。你需要完成student_code.py中的commplex_exp_torch、ifftshift2d_tf、transp_ifft2d、transp_fft2d四个函数，感受通过梯度下降的方式优化硬件参数设计的过程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim\n",
    "from torch.utils.data.dataset import Dataset\n",
    "from torch.utils.data.dataloader import DataLoader\n",
    "import os\n",
    "from tqdm.notebook import tqdm\n",
    "from student_code import *\n",
    "\n",
    "#####################################################\n",
    "# 固定参数，不需要调整\n",
    "padamt = 80\n",
    "otf_size = 142\n",
    "classes = 10\n",
    "\n",
    "#####################################################\n",
    "train_batch_size = 128\n",
    "test_batch_size = 64  \n",
    "\n",
    "learning_rate = 0.00001 \n",
    "\n",
    "#####################################################\n",
    "train_dataset = MyDataset(train=True)\n",
    "test_dataset = MyDataset(train=False)\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=train_batch_size, shuffle=True, pin_memory=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=test_batch_size, shuffle=False, pin_memory=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Torch not compiled with CUDA enabled",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()        \u001b[38;5;66;03m#释放显存\u001b[39;00m\n\u001b[0;32m      6\u001b[0m model \u001b[38;5;241m=\u001b[39m onn()\n\u001b[1;32m----> 7\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m#####################################################\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# 构建损失函数\u001b[39;00m\n\u001b[0;32m     11\u001b[0m criterion \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mCrossEntropyLoss()      \u001b[38;5;66;03m#交叉熵\u001b[39;00m\n",
      "File \u001b[1;32md:\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1152\u001b[0m, in \u001b[0;36mModule.to\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1148\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1149\u001b[0m                     non_blocking, memory_format\u001b[38;5;241m=\u001b[39mconvert_to_format)\n\u001b[0;32m   1150\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, non_blocking)\n\u001b[1;32m-> 1152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:825\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn, recurse)\u001b[0m\n\u001b[0;32m    821\u001b[0m \u001b[38;5;66;03m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[0;32m    822\u001b[0m \u001b[38;5;66;03m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[0;32m    823\u001b[0m \u001b[38;5;66;03m# `with torch.no_grad():`\u001b[39;00m\n\u001b[0;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m--> 825\u001b[0m     param_applied \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    826\u001b[0m should_use_set_data \u001b[38;5;241m=\u001b[39m compute_should_use_set_data(param, param_applied)\n\u001b[0;32m    827\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m should_use_set_data:\n",
      "File \u001b[1;32md:\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1150\u001b[0m, in \u001b[0;36mModule.to.<locals>.convert\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_to_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m t\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m):\n\u001b[0;32m   1148\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1149\u001b[0m                 non_blocking, memory_format\u001b[38;5;241m=\u001b[39mconvert_to_format)\n\u001b[1;32m-> 1150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_complex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Python\\Python311\\Lib\\site-packages\\torch\\cuda\\__init__.py:293\u001b[0m, in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m    288\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    289\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    290\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiprocessing, you must use the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspawn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m start method\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    291\u001b[0m     )\n\u001b[0;32m    292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(torch\u001b[38;5;241m.\u001b[39m_C, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_cuda_getDeviceCount\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 293\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTorch not compiled with CUDA enabled\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _cudart \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    295\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[0;32m    296\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    297\u001b[0m     )\n",
      "\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
     ]
    }
   ],
   "source": [
    "#调用GPU\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "device = torch.device(\"cuda\")\n",
    "torch.cuda.empty_cache()        #释放显存\n",
    "\n",
    "model = onn()\n",
    "model.to(device)\n",
    "\n",
    "#####################################################\n",
    "# 构建损失函数\n",
    "criterion = torch.nn.CrossEntropyLoss()      #交叉熵\n",
    "# 构建优化器\n",
    "optimizer = optim.SGD(model.parameters(),lr=learning_rate,momentum=0.5)    #带动量0.5\n",
    "# 设置学习率梯度下降，如果连续2个epoch测试准确率没有上升，则降低学习率，系数0.5\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2, verbose=True, threshold=0.00005, threshold_mode='rel', cooldown=0, min_lr=1e-7, eps=1e-08)\n",
    "\n",
    "#####################################################\n",
    "train_epoch = []\n",
    "model_accuracy = []\n",
    "temp_acc = 0.0\n",
    "train_loss_val = []\n",
    "max_acc = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch):\n",
    "    running_loss =0.0\n",
    "    for batch_idx,data in enumerate(train_loader,0):\n",
    "        inputs,target = data\n",
    "        inputs,target = inputs.to(device),target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        #forward,backward,update\n",
    "        outputs = model(inputs)\n",
    "        outputs = img_split(outputs)\n",
    "\n",
    "        loss = criterion(outputs,target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss+=loss.item()\n",
    "\n",
    "    tqdm.write('loss: %.3f' % (loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            images,labels = data\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            outputs = img_split(outputs)\n",
    "            \n",
    "            _, predicted = torch.max(outputs.data,dim=1)       #从第一维度开始搜索\n",
    "            _, labels = torch.max(labels, dim=1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted==labels).sum().item()\n",
    "    tqdm.write('Accuracy on test set: %f %% [%d/%d]' % (100 * correct / total, correct, total))\n",
    "\n",
    "    return correct/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
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       "bar_format": null,
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       "elapsed": 0.0073702335357666016,
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       "prefix": "",
       "rate": null,
       "total": 30,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
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      "application/vnd.jupyter.widget-view+json": {
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       "  0%|          | 0/30 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1\n",
      "loss: 0.429\n",
      "Accuracy on test set: 86.000000 % [8600/10000]\n",
      "Epoch: 2\n",
      "loss: 0.425\n",
      "Accuracy on test set: 91.660000 % [9166/10000]\n",
      "Epoch: 3\n",
      "loss: 0.432\n",
      "Accuracy on test set: 90.860000 % [9086/10000]\n",
      "Epoch: 4\n",
      "loss: 0.500\n",
      "Accuracy on test set: 87.680000 % [8768/10000]\n",
      "Epoch: 5\n",
      "loss: 0.414\n",
      "Accuracy on test set: 84.840000 % [8484/10000]\n",
      "Epoch     5: reducing learning rate of group 0 to 5.0000e-06.\n",
      "Epoch: 6\n",
      "loss: 0.353\n",
      "Accuracy on test set: 92.860000 % [9286/10000]\n",
      "Epoch: 7\n",
      "loss: 0.252\n",
      "Accuracy on test set: 93.330000 % [9333/10000]\n",
      "Epoch: 8\n",
      "loss: 0.281\n",
      "Accuracy on test set: 94.280000 % [9428/10000]\n",
      "Epoch: 9\n",
      "loss: 0.402\n",
      "Accuracy on test set: 93.270000 % [9327/10000]\n",
      "Epoch: 10\n",
      "loss: 0.259\n",
      "Accuracy on test set: 94.470000 % [9447/10000]\n",
      "Epoch: 11\n",
      "loss: 0.269\n",
      "Accuracy on test set: 92.990000 % [9299/10000]\n",
      "Epoch: 12\n",
      "loss: 0.278\n",
      "Accuracy on test set: 93.080000 % [9308/10000]\n",
      "Epoch: 13\n",
      "loss: 0.500\n",
      "Accuracy on test set: 92.320000 % [9232/10000]\n",
      "Epoch    13: reducing learning rate of group 0 to 2.5000e-06.\n",
      "Epoch: 14\n",
      "loss: 0.269\n",
      "Accuracy on test set: 94.730000 % [9473/10000]\n",
      "Epoch: 15\n",
      "loss: 0.264\n",
      "Accuracy on test set: 93.990000 % [9399/10000]\n",
      "Epoch: 16\n",
      "loss: 0.188\n",
      "Accuracy on test set: 95.040000 % [9504/10000]\n",
      "Epoch: 17\n",
      "loss: 0.349\n",
      "Accuracy on test set: 94.220000 % [9422/10000]\n",
      "Epoch: 18\n",
      "loss: 0.295\n",
      "Accuracy on test set: 94.290000 % [9429/10000]\n",
      "Epoch: 19\n",
      "loss: 0.286\n",
      "Accuracy on test set: 95.010000 % [9501/10000]\n",
      "Epoch    19: reducing learning rate of group 0 to 1.2500e-06.\n",
      "Epoch: 20\n",
      "loss: 0.226\n",
      "Accuracy on test set: 95.160000 % [9516/10000]\n",
      "Epoch: 21\n",
      "loss: 0.270\n",
      "Accuracy on test set: 95.060000 % [9506/10000]\n",
      "Epoch: 22\n",
      "loss: 0.269\n",
      "Accuracy on test set: 94.550000 % [9455/10000]\n",
      "Epoch: 23\n",
      "loss: 0.183\n",
      "Accuracy on test set: 94.730000 % [9473/10000]\n",
      "Epoch    23: reducing learning rate of group 0 to 6.2500e-07.\n",
      "Epoch: 24\n",
      "loss: 0.242\n",
      "Accuracy on test set: 95.130000 % [9513/10000]\n",
      "Epoch: 25\n",
      "loss: 0.248\n",
      "Accuracy on test set: 95.110000 % [9511/10000]\n",
      "Epoch: 26\n",
      "loss: 0.249\n",
      "Accuracy on test set: 95.310000 % [9531/10000]\n",
      "Epoch: 27\n",
      "loss: 0.203\n",
      "Accuracy on test set: 95.450000 % [9545/10000]\n",
      "Epoch: 28\n",
      "loss: 0.231\n",
      "Accuracy on test set: 95.390000 % [9539/10000]\n",
      "Epoch: 29\n",
      "loss: 0.266\n",
      "Accuracy on test set: 95.270000 % [9527/10000]\n",
      "Epoch: 30\n",
      "loss: 0.220\n",
      "Accuracy on test set: 95.300000 % [9530/10000]\n",
      "Epoch    30: reducing learning rate of group 0 to 3.1250e-07.\n",
      "acc: 0.9545\n"
     ]
    }
   ],
   "source": [
    "for epoch in tqdm(range(30)):\n",
    "    tqdm.write('Epoch: %d' % (epoch + 1))\n",
    "    train(epoch)\n",
    "    acc = test()\n",
    "\n",
    "    if acc > max_acc:\n",
    "        max_acc = acc\n",
    "    train_epoch.append(epoch)\n",
    "    model_accuracy.append(acc)\n",
    "    scheduler.step(acc)\n",
    "print('acc:', max_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()  # 随机获取部分数据\n",
    "image = images[0:2].to(device)\n",
    "\n",
    "# 现在你可以将图片送入模型\n",
    "output = model(image)\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(image[0].reshape(32,32).cpu().numpy(), cmap='gray')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(output[0].squeeze().cpu().detach().numpy(), cmap='gray')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "charmnet",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
